JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D19106, doi:10.1029/2012JD017777, 2012
Why might stratospheric sudden warmings occur with similar
frequency in El Niño and La Niña winters?
C. I. Garfinkel,1 A. H. Butler,2 D. W. Waugh,1 M. M. Hurwitz,3,4 and L. M. Polvani5,6
Received 13 March 2012; revised 3 August 2012; accepted 17 August 2012; published 4 October 2012.
[1] The effect of El Niño-Southern Oscillation (ENSO) on the frequency and character of
Northern Hemisphere major mid-winter stratospheric sudden warmings (SSWs) is
evaluated using a meteorological reanalysis data set and comprehensive chemistry-climate
models. There is an apparent inconsistency between the impact of opposite phases of
ENSO on the seasonal mean vortex and on SSWs: El Niño leads to an anomalously warm,
and La Niña leads to an anomalously cool, seasonal mean polar stratospheric state, but both
phases of ENSO lead to an increased SSW frequency. A resolution to this apparent paradox
is here proposed: the region in the North Pacific most strongly associated with precursors
of SSWs is not strongly influenced by El Niño and La Niña teleconnections. In the
observational record, both La Niña and El Niño lead to similar anomalies in the region
associated with precursors of SSWs and, consistent with this, there is a similar SSW
frequency in La Niña and El Niño winters. A similar correspondence between the
penetration of ENSO teleconnections into the SSW precursor region and SSW frequency is
found in the comprehensive chemistry-climate models. The inability of some of the models
to capture the observed relationship between La Niña and SSW frequency appears
related to whether the modeled ENSO teleconnections result in extreme anomalies in the
region most closely associated with SSWs. Finally, it is confirmed that the seasonal mean
polar vortex response to ENSO is only weakly related to the relative frequency of SSWs
during El Niño and La Niña.
Citation: Garfinkel, C. I., A. H. Butler, D. W. Waugh, M. M. Hurwitz, and L. M. Polvani (2012), Why might stratospheric
sudden warmings occur with similar frequency in El Niño and La Niña winters?, J. Geophys. Res., 117, D19106,
doi:10.1029/2012JD017777.
1. Introduction
[2] The El Niño - Southern Oscillation (ENSO) is the
dominant mode of interannual variability in the Tropics, and
it has well-known teleconnections with the Northern Hemisphere (NH) midlatitudes [Horel and Wallace, 1981]. During
an El Niño winter (hereafter EN), mid-tropospheric geopotential heights are anomalously low in the North Pacific,
1
Department of Earth and Planetary Science, Johns Hopkins University,
Baltimore, Maryland, USA.
2
Climate Prediction Center, NCEP, NOAA, Camp Springs, Maryland,
USA.
3
Goddard Earth Sciences Technology and Research, Morgan State
University, Baltimore, Maryland, USA.
4
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.
5
Department of Applied Physics and Applied Mathematics and
Department of Earth and Environmental Sciences, Columbia University,
New York, New York, USA.
6
Lamont-Doherty Earth Observatory, Columbia University, Palisades,
New York, USA.
Corresponding author: C. I. Garfinkel, Department of Earth and
Planetary Science, Johns Hopkins University, Baltimore, MD 21209, USA.
([email protected])
©2012. American Geophysical Union. All Rights Reserved.
0148-0227/12/2012JD017777
and vice versa for La Niña winters (hereafter LN) (e.g.,
Figures 1a for EN and 1b for LN). Recently, these teleconnections with the midlatitudes have been shown to influence the wintertime NH stratospheric polar vortex [Manzini
et al., 2006; Garfinkel and Hartmann, 2008; Ineson and
Scaife, 2009; Bell et al., 2009]. During LN, opposite-signed,
but weaker, seasonal-mean stratospheric anomalies appear to
be present in both observations and models forced with
observed sea surface temperatures [Sassi et al., 2004; Manzini
et al., 2006; Garfinkel and Hartmann, 2007; Brönnimann,
2007; Free and Seidel, 2009; Mitchell et al., 2011].
[3] Occasionally, the polar vortex completely breaks down,
whereby zonal winds change from strong, climatological
(>40m/s) westerlies to easterlies at 10 hPa, 60 N. Such events
are known as major stratospheric sudden warmings (SSWs),
and are preceded by a burst of wave activity from the troposphere into the stratosphere [Matsuno, 1971]. Nishii et al.
[2009] and Garfinkel et al. [2010] show that on intraseasonal timescales, low height anomalies in the North Pacific can
lead to such a burst of wave activity. Because a trough of the
climatological planetary wave pattern (and of both wave
number 1 and wave number 2 separately) is located in the
North Pacific, an anomalous low in the North Pacific will
enhance the climatological planetary waves in the troposphere.
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Figure 1. Geopotential height anomalies at 500 hPa in NDJFM, in the ECMWF reanalysis data during
(a) a composite of El Niño and (b) La Niña events, and (c) 5–20 days preceding major sudden stratospheric
warmings. The contour interval is 10 m. Squares and stars are included in each plot at 62.5 N, 180 E and
45 N, 205 E; see the text for details. The zero line is shown in black.
Once the resulting stronger planetary wave propagates
upwards and breaks in the stratosphere, it will weaken the
polar vortex. In contrast, an anomalous ridge in this region
leads to an intensification of the vortex [Limpasuvan et al.,
2005; Woollings et al., 2010; Nishii et al., 2010]. Once a
SSW occurs, it can influence jets in the troposphere in the
weeks or months following an event [Polvani and Waugh,
2004; Limpasuvan et al., 2004] and thereby impact surface
climate. A better understanding of the connection between
ENSO and SSWs might therefore lead to improved tropospheric seasonal forecasts for ENSO winters and understanding of Arctic ozone variability [Baldwin et al., 2003; Cagnazzo
et al., 2009].
[4] Butler and Polvani [2011] (hereafter BP11) recently
revisited the ENSO-stratosphere connection and found that
from 1958 to 2010, SSWs occur just as often during EN
winters as during LN winters, contrary to the modeling study
of Taguchi and Hartmann [2006]. BP11 also found that
major SSWs occur nearly twice as often during both EN and
LN winters as compared to ENSO-neutral winters. Even
though EN and LN lead to opposite signed seasonal mean
vortex anomalies, they both lead to more SSWs. Figure 2
displays this graphically: even though the mean state differs between EN and LN winters, the extremes are indistinct.
In contrast, the mean states of ENSO active years (i.e., either
EN or LN) and ENSO neutral years are indistinguishable,
but the vortex is more variable during ENSO active years.
Finally, the SSW frequencies during each ENSO phase are
consistent with the heat flux anomalies in the lower stratosphere: even though the seasonal mean distribution of zonal
wave number-1 and 2 heat flux differs between EN and
LN (Figures 3a and 3b, as in Taguchi and Hartmann [2006]
and Garfinkel and Hartmann [2008]), the frequency of large
positive heat flux anomalies is greater during LN winters
than during neutral ENSO winter (or even during EN
winters).
[5] In this paper, we investigate how (and why) LN and EN
can both lead to increased SSW frequency, and, at the same
time, to opposite signed seasonal mean vortex anomalies. We
also consider whether comprehensive chemistry-climate
models (CCMs) can capture this effect. Section 2 will introduce the data sources and the methods used. Section 3.1 will
Figure 2. Zonal wind at 60N, 10 hPa in the reanalysis data during (left) a composite of El Niño and La
Niña events, and (right) ENSO active and neutral ENSO events. Bold lines show the mean wind, and
dashed lines show the largest extreme. A 7-point running mean filter has been applied in order to smooth
the data. The years selected for each composite can be found in Table 1 and are chosen with the 0.5C
threshold.
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show that the tropospheric teleconnections associated with
ENSO are consistent with an increased SSW frequency
(hereafter, fSSW) in both EN and LN winters in the observational record. We will then test the explanation offered in
Section 3.1 by examining fSSW in CCMs in Section 3.2,
where we will show that fSSW during EN and LN winters in
CCMs is also related to whether ENSO teleconnections result
in extreme anomalies in the region most closely associated
with SSWs. In contrast, the seasonal mean stratospheric
response to ENSO is very weakly related to LN and EN SSW
frequency. Section 3.3 will show that models that do not
capture some of the subtle details of LN teleconnections and
SSWs also fail to capture an enhanced fSSW in LN. The
appendix will discuss the preconditioning of the vortex, the
strength of the SSWs, and the morphology of the SSWs, for
the SSWs during EN and LN.
2. Data and Methods
2.1. Data
[6] We evaluate the connection between ENSO and SSWs
in both the ECMWF reanalysis record (ERA40 [Uppala
et al., 2005] through 2002 and 2 additional years of operational data) and integrations of a range of CCMs. The first
group of models evaluated are the “REF-B1” simulations as
defined by the Chemistry-Climate Model Validation project
phase 2 (CCMVal-2) project [SPARC CCMVal, 2010; Eyring
et al., 2008] and provided to the CCMVal-2 database. The
REF-B1 simulations are driven by annually varying emissions of ozone precursors, concentrations of ozone-depleting
substances and greenhouse gases, sea surface temperatures
(SSTs), and sea ice. We examine the Canadian MiddleAtmosphere Model (CMAM [Scinocca et al., 2008]), Goddard
Earth Observing System CCM, Version 2 (GEOSCCM
[Rienecker et al., 2008; Hurwitz et al., 2010, section 2]),
UMSLIMCAT [Tian and Chipperfield, 2005], CCSRNIES
[Akiyoshi et al., 2009], and UMUKCA-METO [Morgenstern
et al., 2009]. These models are chosen because daily gridded geopotential height in the troposphere is available and
fSSW is at least 3 events per decade (as compared to 6 in
the observational record).
[7] One additional model, LMDZrepro3, meets this criteria, but we do not include it for the following reason. Preliminary work suggests that LMDZrepro3 has too many
splitting type SSWs as opposed to displacement type SSWs
[cf. Charlton and Polvani, 2007]. The tropospheric precursors of SSWs in this model are qualitatively different
from those in other models and in the reanalysis record
(not shown here, but they resemble wave number 2; see
Figures 3b and 3d of Garfinkel et al. [2010] for an example).
Additional work is planned to more fully understand the
ability of this model (and other models in the CCMVal-2
archive) to capture the morphology of SSWs.
[8] In addition, we evaluate three 50-year-long GEOSCCM
integrations in which all boundary conditions are identical
except for the SST and sea ice climatologies, which correspond to perpetual EN, LN and neutral ENSO respectively.
These long integrations are used to demonstrate the robustness
of our results because every winter is in the same ENSO phase.
In the EN experiment, the SSTs of July 1982–June 1983, July
1987–June 1988, and July 1997–June 1998 are composited
together and used to drive the model with a repeating annual
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cycle. In the LN experiment, the SSTs of July 1988–June 1989
and July 1999–June 2000 are composited together and used to
drive the model with a repeating annual cycle. The ENSO
neutral experiment, as well as additional details of the model
integration, are fully described in Hurwitz et al. [2011]. While
the model parameterizations for the perpetual GEOSCCM
integrations are not identical to those used in the experiment
conducted for CCMVal-2 (e.g., the perpetual ENSO experiments include a new gravity wave drag scheme in which a
QBO is internally generated, whereas the CCMVal-2 run does
not include a QBO), the mean state and the variability of the
NH stratosphere is well-reproduced in both. It is likely that
differences between the two sets of results are due to differences in the experimental design rather than differences
between the two model configurations. Specifically, the perpetual ENSO integrations are driven by SST anomalies from a
small number of extreme events, while the CCMVal-2 integration is driven by observed SST from a range of ENSO
events. Finally, we have examined the strength of the correlation between tropospheric variability and weakening of the
vortex (i.e., Figure 1 of Garfinkel et al. [2010]), and
GEOSCCM, unlike WACCM, accurately simulates the magnitude of the correlation. SPARC CCMVal [2010, p. 140]
grades highly the representation of the Northern Hemisphere
stratosphere by the GEOSCCM as compared to the multimodel mean.
2.2. Methods
[9] For the reanalysis and the CCMVal-2 model integrations, winters in which the November through March mean
Niño3.4 index (available at http://www.cpc.ncep.noaa.gov/
data/indices/ersst3b.nino.mth.ascii) exceeds 0.5 C are classified as an EN or LN event when calculating SSW frequency; see BP11 or Table 1 for a list of winters included.
Results are robust to using the NDJ seasonal mean (rather
than NDJFM) and to increasing the Nino 3.4 threshold to
0.75 or 1 (rather than 0.5 C). When we calculate ENSO
teleconnections, we use a Niño3.4 threshold of 1C to show
the effect of ENSO in the North Pacific more clearly. Six LN
events and eight EN events exceeded this threshold; see
Table 1 for a list. Results are robust to using the 0.5 C
threshold. The “REF-B1” simulations covered the period
1960 to 2004; since we wish to include the same ENSO
events for both the models and the reanalysis, we neglect the
SSWs that have occurred in the observational record before
1960 or after 2004. Including these SSWs does not qualitatively affect our results.
[10] Anomalies are computed as follows. For the reanalysis and the CCMVal-2 model integrations, a daily climatology from 1960–2004 is formed and then subtracted from
the raw fields to generate daily anomalies. For the reanalysis
and models with leap years, June 20 of leap years is
neglected. Results are identical if we smooth the daily climatology or if we subtract the 4 highest harmonics of the
annual cycle to form anomalies. For the GEOSCCM perpetual ENSO experiments, a daily climatology of the ENSO
neutral integration is formed and subtracted from the LN and
EN integrations to generate daily anomalies. The seasonal
mean polar vortex strength is defined as the 10 hPa,
NDJFM, 70N and poleward area weighted average of the
daily anomalies of geopotential height. Other wintertime
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Table 1. Frequency (Freq) of SSW for the Observations (1960–2004 Only), the Perpetual ENSO GEOSCCM Integrations, and the
CCMVal Integrationsa
SSW Frequency in Each Data Source
EN
Reanalysis
GEOSCCM perp. ENSO
GEOSCCM
UMSLIMCAT
CMAM, Ens. mean
UMUKCA-METO
CCSRNIES, Ens. mean
LN
Neutral
All
Winters
SSW
Freq
Winters
SSW
Freq
Winters
SSW
Freq
Winters
SSW
Freq
14
50
14
14
42
14
42
10
35
11
13
41
13
21
0.71
0.70
0.79
0.93
0.98
0.93
0.50
15
50
15
15
45
15
45
11
9
7
12
46
6
13
0.73
0.18
0.47
0.80
1.02
0.40
0.29
15
50
15
15
45
15
45
5
16
8
9
40
9
13
0.33
0.32
0.53
0.60
0.89
0.60
0.29
44
150
44
44
132
44
132
26
60
26
34
127
28
47
0.59
0.40
0.59
0.77
0.96
0.64
0.36
a
The El Niño winters are 1963/64, 1965/66, 1968/ 69, 1969/70, 1972/73, 1976/77, 1977/78, 1982/83, 1986/87, 1987/88, 1991/92, 1994/95, 1997/98, and
2002/03. The 15 La Niña winters are 1962/63, 1964/65, 1967/68, 1970/71, 1971/72, 1973/74, 1974/75, 1975/76, 1983/84, 1984/85, 1988/89, 1995/96,
1998/99, 1999/2000, and 2000/01. The remaining winters are classified as ENSO neutral. The 8 El Niño winters in which Niño3.4 exceeds 1 C are
1965/66, 1972/73, 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, and 2002/03. The 6 La Niña winters in which Niño3.4 is less than 1 C are 1970/71,
1973/74, 1975/76, 1988/89, 1998/99, and 1999/2000.
seasonal mean quantities are computed by averaging the
daily anomalies during each NDJFM period.
[11] Three markers are placed on Figures 1, 6, and 7 to
indicate important locations to which we refer throughout
the paper. The 500 hPa height anomalies 5 to 20 days preceding SSWs in the reanalysis are composited together, and
the center of the 20 latitude by 30 longitude region with
the largest anomaly is indicated by a square. The square is
located at 62.5 N, 180 E (i.e. the box extends from 52.5 N–
72.5 N, 165 E–195 E) and this location is marked with a
square on all of the panels. This region is referred to as the
SSW precursor region. A similar procedure is followed for
each CCM, and a diamond is used to indicate the center of
the SSW precursor region for each CCM. While some previous authors have separated the tropospheric precursors
associated with split or displacement type SSWs [e.g.,
Cohen and Jones, 2011], we do not for two reasons. First,
the North Pacific is important for both wave-1 and wave-2
wave driving of the vortex and for both split and displacement type SSWs [Garfinkel et al., 2010; Cohen and Jones,
2011]. Second, there is no strong preference for either type
of SSW during either LN or EN (see the appendix for more
details). Finally, the SSW precursor region in NASA’s
Modern-Era Retrospective Analysis for Research and
Applications (MERRA) [Rienecker et al., 2011] reanalysis is
nearly identical [Garfinkel et al., 2012].
[12] The star is used to indicate the center of the 20 latitude by 30 longitude region in which the difference between
EN and LN height anomalies is maximized in the reanalysis
record (i.e. 45 N, 205 E, the center of 35 N–55 N, 190 E–
220 E). This location is referred to as Gulf of Alaska center of
ENSO teleconnections. Results are robust to alteration of the
size of the boxes. The location of the square and the star are
the same in all figures, but the location of the diamond
changes with each data source. Section 3 will discuss the
relative ability of different phases of ENSO to influence
anomalies in these regions.
3. ENSO and Tropospheric Precursors to SSWs
3.1. Observations
[13] In this section we develop an explanation for the
similar fSSW in EN and LN winters in the observational
record. We will then test this explanation in Section 3.2.
Figures 1a and 1b show the teleconnections associated with
LN and EN, and Figure 1c shows geopotential height
anomalies at 500 hPa preceding SSWs (independent of
ENSO phase). Prior to SSWs, a deep subpolar low height
anomaly is centered just west of the dateline (denoted by a
square on Figure 1). As discussed in the introduction,
anomalously deep lows in this region constructively interfere
with the stationary waves and weaken the vortex [Garfinkel
and Hartmann, 2008; Ineson and Scaife, 2009; Nishii et al.,
2009; Garfinkel et al., 2010]. Regardless of ENSO phase,
low height anomalies in the North Pacific are present
throughout the period in which the vortex is weakening (not
shown). The key point of Figure 1 is that the anomalies preceding SSWs are different from the LN and EN teleconnections; namely, the anomalous low that precedes SSWs (the
square) is to the northwest of the region where ENSO teleconnections are strongest (the star).
[14] While the teleconnections of EN and LN are to first
order linear (i.e. equal in magnitude and opposite in sign),
important deviations from linearity exist in the Northwest
Pacific. Most importantly for SSWs, the anomalous low
associated with EN extends into the subpolar Northwest
Pacific where low anomalies can constructively interfere
with the climatological planetary waves and thus increase
planetary wave driving of the stratosphere. In contrast, the
anomalous ridge associated with LN does not reach this
region. The pattern correlation in the region 40N–75N,
165E–215E between LN’s teleconnections and the anomalies preceding SSWs is 0.01. The difference between EN and
LN teleconnections is significant only closer to the Gulf of
Alaska. (We note, in passing, that our LN composite pattern
differs in its details from that in Hoerling et al. [1997]. This
difference could be due to the modern reanalysis product
used here or due to the winter seasons chosen- 4 of the 9 LN
winters in Hoerling et al. [1997] predate 1960 and are
therefore outside of our analysis period.)
[15] It is conceivable that SSWs are caused mainly by
extreme height anomalies, as linear quasi-geostrophic
Eliassen Palm flux is related to the square of wave amplitude
[Dunkerton et al., 1981; Andrews et al., 1987, pp. 188 and
231]. We therefore compute the probability of extreme
negative height anomalies in the SSW precursor region
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Figure 3. Heat flux (v′ T ′ ) during EN, LN, and neutral ENSO at 70 hPa. (a) Seasonal mean (November–
March) for zonal wave number 1, (b) like Figure 3a but for zonal wave number 2. (c) Number of days per
winter in which heat flux anomalies exceed 15Km/s. Anomalies are computed as in section 2, and results
are robust to changing the threshold to e.g., 10Km/s or 20Km/s. The years selected for each composite can
be found in Table 1 and are chosen with the 0.5C threshold.
during EN winters, LN winters, and neutral ENSO winters.
The percentage of winter days in which height anomalies in
the SSW precursor region (i.e. surrounding the square on
Figure 1) exceed 120 m is evaluated. Perhaps surprisingly,
extreme negative anomalies occur most often during LN
winters, and more often during EN winters than neutral
ENSO winters, consistent with the respective SSW frequencies. The next section (and in particular Figures 5a, 5d,
and 5e) will discuss this effect more quantitatively, but e.
g., the increase during LN as compared to neutral ENSO
is 20%. (Results are not sensitive to the threshold for
classifying an extreme height anomaly.)
[16] A similar procedure can be followed for extreme
height anomalies in the Gulf of Alaska in the region where
ENSO teleconnections peak (denoted with a star on
Figure 1). ENSO influences extreme height anomalies in the
Gulf of Alaska; extreme negative anomalies occur in this
region most often during EN and least often during LN
(specifically, the frequency during EN is more than double
that of LN, and this difference is significant at the 95% level
by a Monte Carlo test in which winters are randomly characterized as EN or LN). However, the Gulf of Alaska is at
the node of the pattern associated with tropospheric precursors of SSWs shown in Figure 1c. In contrast, LN is
associated with a slight increase in frequency of extreme
negative events in the SSW precursor region, consistent with
the enhanced fSSW during LN. In the next section we will
show that the LN to EN ratio of extreme height anomalies in
the SSW precursor region is closely associated with the LN
to EN ratio of fSSW in models as well.
3.2. Models
[17] We now discuss the fSSW during ENSO in a number
of model integrations. Table 1 lists fSSW for each model
considered, and these results are presented visually in
Figure 4. While some models accurately capture the
observed fSSW during the different ENSO phases (e.g.,
UMSLIMCAT), others do not (e.g., GEOSCCM and
UMUKCA-METO). Using a Monte Carlo test to count
SSWs in 10,000 random winters equal to the length of
the GEOSCCM perpetual ENSO integrations (i.e. 50 years),
the probability of a decrease in fSSW during LN relative to
the other two integrations is found to be p < 0.001. Hence,
some physical mechanism is almost certainly responsible
for the reduced fSSW during LN in this model. An
additional question we will address is: what might cause the
inter-model variability in EN and LN SSW frequency?
[18] In Figure 5, we consider the relationship between LN
and EN fSSW and the tropospheric teleconnections of
ENSO for the models in Table 1 and thereby test the
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Figure 4. The frequency of sudden stratospheric warming events during La Niña, El Nino, and neutral
ENSO winters in the reanalysis record from 1960–2004, in three 50-year-long perpetual ENSO
GEOSCCM integrations, and in models discussed in the CCMVal-2 report. A dashed line represents
the average SSW frequency in the reanalysis record (cf. Table 1).
explanation presented in Section 3.1. We first show that
the explanation presented in Section 3.1 is able to explain the
intraensemble variability in EN and LN SSW frequency.
Then, in Section 3.3, we will discuss each model
individually.
[19] Consider first Figure 5a, where we plot the LN to EN
ratio of extreme height anomalies in the SSW precursor
region versus the ratio of fSSW. The correlation between the
two is shown in the panel title. It is clear from the figure that
the LN to EN ratio of extreme height anomalies in the
Figure 5. The relationship between LN and EN SSWs and the tropospheric and seasonal mean stratospheric effect of ENSO in NDJFM for each data source. Each marker represents one data source (i.e.
reanalysis data or a model). (top)The relationship between the ratio of LN SSW frequency to EN SSW frequency and (a) the ratio of LN to EN extreme negative height anomalies in the SSW precursor region, (b)
the correlation between the seasonal mean ENSO value and the seasonal mean Gulf of Alaska (35 N–
55 N, 190 E–220 E) height anomaly, and (c) like Figure 5b but of ENSO with the seasonal mean polar
vortex strength. (bottom) Like Figure 5a except for (d) the ratio of LN to neutral ENSO and (e) the ratio
of EN to neutral ENSO. Note that diagnostics for the perpetual ENSO GEOSCCM experiment are only
shown in Figures 5a, 5d, and 5e because of the nature of the experimental design.
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tropospheric SSW precursor region is highly correlated with
the LN to EN ratio of SSWs (p > .98). This strongly suggests
that the inter-model variability in EN and LN fSSW is
related to inter-model variability in the ability of ENSO
teleconnections to penetrate the SSW precursor region. The
explanation offered in Section 3.1 appears to explain the
behavior in the other data sources as well.
[20] One might hypothesize that a model in which ENSO
has a stronger impact on the Gulf of Alaska region might
also have a larger difference between EN SSW and LN SSW
frequency. To test this, we evaluate whether the strength of
ENSO’s teleconnection in the Gulf of Alaska region
(defined in Section 2), rather than in the SSW precursor
region, might be a better predictor of the ratio of LN to EN
SSW frequency. Figure 5b shows the correlation of seasonal
mean height anomalies in the Gulf of Alaska and the seasonal mean value of ENSO on the abscissa, and the ratio of
LN to EN fSSW on the ordinate. The strength of ENSO
teleconnections appears well correlated with the ratio of LN
to EN fSSW (with p > 0.8). However, the inter-model variability in LN to EN fSSW is explained more closely by the
frequency of extreme negative events in the SSW precursor
region (i.e. Figure 5a). This clearly indicates that the frequency of extreme negative events in the SSW precursor
region is more closely related to LN and EN fSSW than the
impact of ENSO in the Gulf of Alaska.
[21] We next consider the relationship between the seasonal mean stratospheric response to ENSO and the effect of
ENSO on SSWs. One might hypothesize that the seasonal
mean stratospheric response to ENSO and the effect of
ENSO on SSWs are closely related, and that a data set with a
larger LN fSSW must necessarily have a weaker seasonal
mean stratospheric vortex during LN. To demonstrate that
this is not the case, Figure 5c compares the correlation
between ENSO and the seasonal mean stratospheric vortex
strength (defined in Section 2) to the ratio of LN to EN
fSSW in each model. We find that the seasonal mean
stratospheric response is not correlated with the ratio of LN
to EN SSW frequency. Models that capture the seasonal
mean stratospheric response to ENSO do not necessarily
capture the correct LN to EN SSW frequency, and vice
versa. The independence of the effect of ENSO on seasonal
mean vortex strength from the effect of ENSO on SSW is
consistent with the independence on interannual timescales
found in BP11. In addition, Charlton and Polvani [2007]
(see their see section 5a) find that at 10 hPa, the recovery
from a SSW is sufficiently fast that the occurrence of a SSW
has a weak effect on the seasonal mean vortex strength.
They also find little relationship between seasonal mean
wave driving of the vortex (which is modulated by ENSO)
and the occurrence of a SSW. Finally, Figure 3 also
demonstrates that the effect of ENSO on lower stratospheric
heat flux differs between the seasonal mean and the intraseasonal extremes.
[22] BP11 also found that fSSW during neutral ENSO
winters was lower than during either EN or LN winters. We
test whether our explanation might be applicable to this
effect as well. Figure 5d shows the relationship between
extreme height anomalies in the SSW precursor region for
each model during neutral ENSO and LN winters and the
ratio of neutral ENSO to LN SSW frequency. The neutral
ENSO to LN ratio of extreme height anomalies in the
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tropospheric SSW precursor region is highly correlated with
the neutral ENSO to LN ratio of SSWs, though one model
appears to be an outlier. This suggests that the inter-model
variability in neutral ENSO fSSW is related to inter-model
variability in the frequency of extreme anomalies in the
SSW precursor region during neutral ENSO winters. Finally,
Figure 5e is similar to Figure 5d, but shows the ratio of
neutral ENSO to EN winters. Again, we find that the ratio of
extreme height anomalies in the SSW precursor region is
highly correlated with the ratio of SSW frequency, with the
same model again an outlier. In summary, the intra-ensemble
variability in the ratio of LN, neutral ENSO, and EN fSSW is
closely linked to whether ENSO teleconnections result in
extreme anomalies in the SSW precursor region.
3.3. Understanding SSW Frequency in Each Model
[23] In this section, we consider the LN and EN fSSW for
each model in the context of the explanation we have presented. We will show that models that capture the processes
highlighted above are able to capture the observed relationship between ENSO and SSW frequency. First, a model
must realistically simulate the tropospheric teleconnections
in the subpolar North Pacific in response to EN and LN.
Second, a model must realistically simulate the tropospheric
precursors of SSW events. If either of these processes is
poorly simulated by a model, we find that that the resulting
EN and/or LN fSSW does not match the observations.
[24] Figures 6 and 7 show the ENSO teleconnections and
SSW precursors for the models in Table 1, with one model
for each row in the figures. The SSW precursor anomalies
are generally captured in the CCM integrations (Figure 6,
right, and 7, right); the general agreement is indicated by the
large pattern correlation with the reanalysis (in Figure 1c)
shown on the right panel of each row and can be seen in the
proximity of the diamond to the square. In addition, all
models capture the gross features of EN and LN teleconnections, namely a trough and ridge respectively in the
Gulf of Alaska near the star (though the magnitude certainly
varies, as in Cagnazzo et al. [2009]). These gross similarities
notwithstanding, subtle differences exist between the models, however: we now focus on these subtle differences and
thereby shed light on the simulated fSSW during EN and LN
in each model.
[25] 1. In the GEOSCCM perpetual-ENSO experiments
(Figure 6, top), the modeled EN teleconnection and tropospheric precursors of SSWs are grossly similar to the
observations. However, the modeled LN teleconnection is
not true to observations in the Northwest Pacific (at the
square on Figure 1b versus Figure 6b). Namely, the anomalous ridge generated by LN in the GEOSCCM model
extends into the SSW precursor region in the subpolar
Northwest Pacific. More importantly for SSWs, LN events
lead to a statistically significant reduction in extreme negative height anomalies in the SSW precursor region (see the
star in Figure 5a). It is therefore expected that fSSW in EN,
but not in LN, is similar to that in the observations, as
demonstrated in Figure 4b. Even though GEOSCCM captures ENSO teleconnections in the Gulf of Alaska, the teleconnections in the Northwest Pacific in LN, and hence the
fSSW, do not resemble that in the reanalysis.
[26] 2. In the GEOSCCM CCMVal2 integration (Figure 6,
middle row), the ENSO teleconnections are similar to,
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Figure 6. Like Figure 1 but for 3 CCMs. (a–c) For the perpetual El Niño and La Niña integrations as
compared to the perpetual neutral ENSO integration (e.g., perp. EN-perp. ENSO neutral). (d–f) For the
GEOSCCM CCMVal-2 integrations. (g–i) For the UMSLIMCAT. The contour interval is 10 m. Squares
and stars are as in Figure 1, but diamonds represent the SSW precursor region for each model; see the text
for details. The pattern correlation in the region 40N–75N, 165E–215E between the tropospheric precursors in the reanalysis and in each model is shown below each plot.
though weaker than, the ENSO teleconnections in the perpetual ENSO experiments. The tropospheric precursors of
SSWs are also similar. The anomalous ridge generated by
LN in the GEOSCCM model extends into the subpolar
Northwest Pacific SSW precursor region. Because extreme
negative anomalies in the SSW precursor region are suppressed, SSWs are suppressed during LN in the GEOSCCM
CCMVal-2 integration as well (Figure 4c).
[27] 3. In UMSLIMCAT (Figure 6, bottom), the anomalous ridge generated by LN does not extend to the subpolar
North Pacific, and the SSW precursors are captured realistically. Because the anomalous ridge generated by LN is
meridionally confined (compared to, e.g., GEOSCCM),
extreme negative height anomalies in the SSW precursor
region occur with similar frequency during LN winters and
EN winters (see the cross in Figure 5a). It is therefore to be
expected that fSSW is similar in EN and LN (Figure 4d).
[28] 4. CMAM realistically captures SSW precursors
(Figure 7c). Even though SSWs occur too frequently in
CMAM (and therefore SSWs could conceivably be less
sensitive to tropospheric variability), the characteristic tropospheric precursors of SSWs are present in CMAM.
CMAM appears to capture the subpolar teleconnections of
LN, though there is some intra-ensemble variability (not
shown) and the anomalies are too weak. Most importantly
for SSWs, however, the frequency of extreme negative
height anomalies in the SSW precursor region is similar in
all phases of ENSO (e.g., see the circle in Figures 5a and 5f).
It is therefore to be expected that fSSW is similar in EN,
neutral ENSO, and LN (Figure 4e).
[29] 5. SSW precursors in UMUKCA-METO (Figure 7f)
do not resemble those in the reanalysis (the pattern correlation between Figure 7f and Figure 1c is lower for
UMUKCA-METO than for any other model). Instead of an
anomalous trough in the SSW precursor region, an anomalous trough is found in the Gulf of Alaska, near the center of
the ridge associated with LN. The diamond is adjacent to the
star and not to the square. The pattern correlation between
Figures 7e and 7f in the region 40N–75N, 165E–215E is
very low ( 0.19). Consistent with this, extreme negative
height anomalies occur less frequently in the SSW precursor
region during LN winters and more frequently during EN
winters (see the triangle in Figure 5a). It is therefore
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Figure 7. Like Figure 6 but for three other CCMVal-2 model integrations: (a–c) CMAM,
(d–f) UMUKCA-METO, and (g–i) CCSRNIES.
expected that in UMUKCA-METO, LN suppresses SSWs
(Figure 4f).
[30] 6. Finally, SSW precursors in CCSRNIES (Figure 7i)
are displaced to the northwest of those in the reanalysis and
are located outside of the region most strongly influenced by
ENSO. In addition, ENSO teleconnections in this model are
too weak. Neither process is captured well as compared to the
reanalysis. Most importantly for SSWs, however, extreme
negative height anomalies occur less frequently in the SSW
precursor region during LN winters as compared to EN
winters (see the “x” in Figure 5a), consistent with the
decreased fSSW during LN (Figure 4g).
[31] In summary, models with realistic tropospheric teleconnections in the subpolar North Pacific during LN winters
and realistic tropospheric precursors of SSWs have realistic
fSSW in LN winters. This affirms the explanation offered in
Section 3.1.
4. Conclusions
[32] Reanalysis data and chemistry-climate models are
used to understand the connection between sudden stratospheric warmings and teleconnection patterns due to ENSO.
The key point of this paper is that the region in the North
Pacific most strongly associated with SSWs (the SSW
precursor region) is distinct from the region most influenced
by El Niño and La Niña. In the reanalysis, La Niña’s
canonical North Pacific ridge does not reach the subpolar
Northwestern Pacific region that most strongly forces SSWs.
Rather, we find that both El Niño and La Niña lead to more
frequent extreme low anomalies in the SSW precursor
region (cf. Figures 5a, 5d, and 5e). This explains why the
frequency of SSWs is increased during both El Niño and La
Niña winters.
[33] In the ensemble of models examined, the inter-model
variability in La Niña and El Niño SSW frequency is found
to be tightly correlated with the ability of ENSO teleconnections to penetrate the SSW precursor region. In
addition, the ratio of SSW frequency during neutral ENSO
and La Niña winters is tightly correlated with anomalies in
the SSW precursor region. Many of the models here examined fail to capture this effect, and they therefore fail to
realistically simulate El Niño and La Niña SSW frequency.
For example, in two versions of the GEOS CCM, the ridge
of La Niña’s teleconnection extends beyond the Northwest
Pacific to Eastern Russia, and so SSWs occur less frequently
in La Niña winters than in El Niño winters. In addition, we
confirm previous studies who have shown that the seasonal
mean stratospheric vortex response to ENSO is not closely
related to the effect of ENSO on SSWs.
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[34] However, we acknowledge that we are unable to rule
out the possibility that, for example, the GEOSCCM model
is accurately simulating La Niña teleconnections and that the
observed frequency of SSWs during La Niña winters is an
artifact of the relatively short data record [note that the difference between LN and neutral ENSO fSSW is significant
at the 90% level in the reanalysis record [Butler and Polvani,
2011]. Although Hoskins and Karoly [1981], Sardeshmukh
and Hoskins [1988], and Simmons et al. [1983] provide a
theory that can successfully explain the gross features of the
wave train generated by anomalous ENSO convection, we
are not aware of a theory that can explain the details of the
subpolar Northwestern Pacific response to ENSO. Furthermore, different ensemble members of the same model can
differ in the subpolar extent of ENSO teleconnections (not
shown). It is therefore not entirely clear whether the “true”
response to La Niña in the Northwestern Pacific more closely
resembles the response in GEOSCCM or the response in the
reanalysis (or in, e.g., UMSLIMCAT). For example, small
changes in the strength or position of the climatological
subtropical jet can lead to altered wave propagation (e.g., due
to the QBO in Garfinkel and Hartmann [2010] or due to
doubled CO2 in Meehl et al. [2006]), and the GEOSCCMclimatological state in the North Pacific, like that in any
model [e.g., Ting and Sardeshmukh, 1993], is not perfect. On
the other hand, the observed relative frequencies of extreme
negative height anomalies in the SSW precursor region in the
different ENSO phases are not statistically distinct. Longer
model integrations and more observational data are necessary
before these results can be stated unequivocally. Nevertheless, the two processes identified in this study - the subpolar
extent of La Niña teleconnections and tropospheric precursors of SSWs - will very likely be important pieces of the
puzzle connecting ENSO and SSW events in the future.
Appendix A: Stratospheric Anomalies During
SSWs in LN and EN
[35] One might ask whether the types of EN and LN SSWs
are distinct and whether qualitatively different precursors are
associated with SSWs in each ENSO phase [see, e.g., Cohen
and Jones, 2011]. We therefore briefly document the type of
the SSWs during LN and EN winters in the observational
record. LN and EN SSWs prior to 2003 are evenly split
between splitting events and displacement events [cf. Charlton
and Polvani, 2007]. Specifically there were 5 split and 6 displacement during EN and 7 split and 4 displacement during
LN in the ERA-40 reanalysis, and 5 split and 5 displacement
during EN and 6 split and 5 displacement during LN in the
NCAR/NCEP reanalysis [Kalnay et al., 1996]. When we
include the winters since 2002 (type taken from Table 1 of
Cohen and Jones [2011]), we find a similar ratio of split versus
displacement events during EN and LN. We have examined
the tropospheric precursors for LN and EN SSWs, and while
height anomalies in the troposphere bear a slight wave-2 signature during LN (not shown), the tropospheric precursors
discussed in Section 3 dominate the LN SSWs as well. Finally,
we note that differences in SSW type among the different
phases of ENSO are not present in the long GEOSCCM perpetual ENSO experiments (not shown), even though the
GEOSCCM is capable of capturing wave-1 and wave-2 patterns associated with SSWs [e.g., Hurwitz et al., 2012]. While
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Figure A1. Anomalies of polar cap geopotential height at
7hpa leading up to a SSW for El Niño, La Niña, and Neutral
ENSO only. Statistically significant differences at the 95%
(90%) level between the El Niño and La Niña responses
are indicated with diamonds (cross). Statistical significance
is determine with a 2-tailed Student-T test.
there might be subtle differences between the type of EN and
LN SSW, these differences are not statistically robust and do
not appear to be related to SSW frequency. Finally, Garfinkel
et al. [2010] found that Northwest Pacific variability is
important for wave driving associated with both wave-1 and
wave-2. We therefore do not condition the SSW precursors
based on the different SSW types.
[36] We now discuss the possible differences in the preconditioning of the vortex; that is, does the vortex strength a
month before the SSWs differ between EN and LN SSWs?
We find that the preconditioning of the vortex is roughly
similar between LN winters and EN winters at 30–40 day
lead times, though the vortex in LN is stronger at intermediate leads, however (Figure A1). In contrast, the preconditioning of neutral ENSO SSWs is dramatically (and
significantly) different: the vortex begins weakening over a
month before the central date. Neutral ENSO SSWs are also
more likely to be displacement events (4 displacement versus 1 split); whether this difference can explain the different
types for neutral ENSO SSWs, or is expected to persist in
future neutral ENSO SSWs, is a topic for future work. We
find that the seasonal mean vortex is strengthened by LN in
all models examined in Sections 3.2–3.3 except for
UMSLIMCAT (e.g., Figure 5c), yet UMSLIMCAT still has
a high LN SSW frequency. Preconditioning of the vortex is
therefore likely not the dominant cause for the enhanced LN
SSW frequency. Finally, during all phases of ENSO, SSWs
are of roughly similar strength (Figure A1).
[37] Acknowledgments. This work was supported by NASA grant
NNX06AE70G and NSF grant ATM 0905863. The work of L.M.P. is
funded, in part, by a grant of the US National Science Foundation to
Columbia University. We thank Andrew Charlton-Perez for providing the
SSW central dates for the CCMVal-2 models.
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